gpt_critique

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It adds useful behavioral context such as 'single-pass' and 'adversarial', but it does not disclose the output format, limitations, or whether the input is mutated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two tight sentences that front-load the core purpose and immediately enumerate the kinds of issues identified. There is no wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with well-described parameters, the description conveys the core action and output types. However, without an output schema, it does not clarify what the critique response looks like, and it misses any mention of the sibling tools or alternative use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides descriptions for all three parameters (content, context, focus_areas), yielding roughly 100% coverage, so the baseline is 3. The description's mention of issue types loosely maps to the focus_areas enum but adds no additional parameter-level meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('critique') and resource ('content'), and enumerates specific output types (logical gaps, edge cases, security issues, incorrect assumptions). However, it does not explicitly distinguish the tool from sibling tools gpt_verify or debate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus its siblings, nor does it state when not to use it or any prerequisites. The intended use is only implied by the word 'critique'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.